博碩士論文 109460013 詳細資訊




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姓名 趙千儀(Chien-Yi Chao)  查詢紙本館藏   畢業系所 會計研究所企業資源規劃會計碩士在職專班
論文名稱 以人臉照片辨識不良寵物飼主之研究
(Identifying bad pet owners from pictures with CNN models)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-6-15以後開放)
摘要(中) 隨著人口結構和社會經濟型態的改變,越來越多人選擇飼養寵物作為生活伴侶,擁有寵物也已經被證實具有許多的好處。然而,雖然很多人都知道擁有寵物可以帶來許多好處,但寵物遺棄、虐待動物和不當飼養事件至今還是相當普遍。而絕大多數飼主其實就是潛在的棄養者、施虐者,我們無法知道哪一位飼主在未來某一天會棄養,甚至虐待自己的寵物,若是能將潛在不良飼主第一時間排除在外,在第一步就做好預防,進而能有效抑制一連串問題的發生。

因此,本研究嘗試透過社群媒體上蒐集黑名單白名單人臉照片,利用深度學習中的卷積神經網路(CNN)演算法進行寵物飼主的好壞辨識。本文的人臉識別CNN 模型結構,由六個卷積層和四個池化層,以及全連接層中的兩個隱藏層組成,我們使用了 Data augmentation(數據增強)來解決數據集不足的問題,使用Batch Normalization(批次正規化)克服模型難以訓練的問題,使用 Dropout 等方法減緩過擬合,並且使用 Adam 優化器和 Softmax 分類器進行人臉識別可以使訓練更穩定、更快收斂,有效提高準確率。

透過卷積神經網路很強的特徵提取圖像辨識的方式,可以快速有效地對寵物飼養人進行辨別。實驗結果表明,本研究 CNN 模型在寵物飼主人臉照片上的識別率為 80.09%。
摘要(英) Along with the change in population structure and the social-economic pattern, adopting pets have become popular. However, as raising pets requires long term devotion and commitment, some pet owners may change their minds or may resort to abusing pets to release their emotion. To preventing pets from becoming the victims of bad owners, how to deter the potential abusers to adopt pets has become an important issue. As people with unstable emotion tend to have certain types of facial expression, this study proposed to build up discriminant model for bad owners by analyzing pictures. Even though analyzing facial images with Nero networks have been studied in many areas, to the best of our knowledge, no one has applied related knowledge to this issue.

The proposed model utilizes CNN with six Convolution layers, four Pooling layers, and two Fully Connected Layer at the end. As the number of pictures was not sufficient, Data augmentation was utilized to increase the data size. Batch Normalization is utilized to quickly converge the model parameters as the number of data are still relative limited. Dropout and regularization methods are also adopted to relieved the issue of overfitting. Numerous hyper parameters tuning were attempted, and result showed that the accuracy can reach 80.09%.
關鍵字(中) ★ 寵物棄養
★ 虐待動物
★ 不當飼養
★ 飼主
★ 深度學習
★ 人臉辨識
★ 卷積神經網路
關鍵字(英) ★ Pet abandon
★ Pet abuse
★ Improper feeding
★ Owner of the pet
★ Deep learning
★ Convolutional Neural Network
★ CNN
論文目次 中文摘要 .................................................................................i
Abstract..................................................................................ii
誌謝......................................................................................iii
目錄......................................................................................v
圖目錄 ...................................................................................vii
表目錄 ...................................................................................viii
第一章 緒論 ..............................................................................1
1-1 研究背景與動機....................................................................1
1-2 研究目的 .........................................................................7
1-3 研究架構 .........................................................................7
第二章 文獻探討 ..........................................................................8
2-1 棄養、虐待及不當飼養行為之意向 ...................................................8
2-1-1 棄養動物行為 ...............................................................8
2-1-2 虐待動物行為 ...............................................................9
2-1-3 不當飼養行為 ...............................................................10
2-2 深度學習卷積神經網路在人臉識別之研究 ............................................ 12
第三章 研究方法 ..........................................................................15
3-1 研究流程 .........................................................................15
3-2 卷積神經網路......................................................................16
3-2-1 卷積層 .....................................................................17
3-2-2 池化層 .....................................................................18
3-2-3 全連接層 ...................................................................19
3-2-4 激活函數 ...................................................................20
第四章 研究實驗分析及結果 ................................................................21
4-1 資料蒐集及預處理..................................................................21
4-2 研究工具 .........................................................................23
4-3 研究過程與分析....................................................................24
4-3-1 第一個實驗階段..............................................................26
4-3-2 第二個實驗階段..............................................................28
4-3-3 第三個實驗階段..............................................................30
4-3-4 第四個實驗階段..............................................................32
4-3-5 第五個實驗階段..............................................................34
4-3-6 第六個實驗階段..............................................................36
4-4 模型評估及預測....................................................................38
第五章 結論 ..............................................................................40
5-1 研究結論 .........................................................................40
5-2 研究限制與未來建議................................................................41
5-2-1 研究限制 ...................................................................41
5-2-2 未來建議 ...................................................................42
第六章 參考文獻 ..........................................................................43
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指導教授 許秉瑜(Ping-Yu Hsu) 審核日期 2022-6-21
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